This type of prediction explanation is quite interesting, as we are aiming to facilitate the understanding of any machine learning models for users without particular knowledge on data analysis or machine learning. Thus, it is more relevant to focus on the works as [1]} or [2]}, cited as additive methods, as they generate a simple set of importance weights for each attribute. This set of weights is easy to interpret, even for someone without expertise in machine learning. Yet, these methods have a major deterrent: their complexity makes them difficult to use for the average user. That is why [3]} explored methods to generate explanations faster, but at the cost of very restricting hypotheses, as the independence of each attribute of the dataset, or the linearity of the model, which is not always the case.
